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Outputs (49)

Rationalising decision-making about risk: a normative approach. (2018)
Conference Proceeding
M'MANGA, A., FAILY, S., MCALANEY, J. and WILLIAMS, C. 2018. Rationalising decision-making about risk: a normative approach. In Clarke, N.L. and Furnell, S.M. (eds.) Proceedings of the 12th International symposium on human aspects of information security and assurance (HAISA 2018), 29-31 August 2018, Dundee, UK. Plymouth: University of Plymouth, pages 263-271. Hosted on the CSCAN Archive [online]. Available from: https://www.cscan.org/?page=openaccess&eid=20&id=395

Techniques for determining and applying security decisions typically follow risk-based analytical approaches where alternative options are put forward and weighed in accordance to risk severity metrics based on goals and context. The reasoning or val... Read More about Rationalising decision-making about risk: a normative approach..

Informed pair selection for self-paced metric learning in Siamese neural networks. (2018)
Conference Proceeding
MARTIN, K., WIRATUNGA, N., MASSIE, S. and CLOS, J. 2018. Informed pair selection for self-paced metric learning in Siamese neural networks. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence XXXV: proceedings of the 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in computer science, 11311. Cham: Springer [online], pages 34-49. Available from: https://doi.org/10.1007/978-3-030-04191-5_3

Siamese Neural Networks (SNNs) are deep metric learners that use paired instance comparisons to learn similarity. The neural feature maps learnt in this way provide useful representations for classification tasks. Learning in SNNs is not reliant on e... Read More about Informed pair selection for self-paced metric learning in Siamese neural networks..

Risk information recommendation for engineering workers. (2018)
Conference Proceeding
MARTIN, K., LIRET, A., WIRATUNGA, N., OWUSU, G. and KERN, M. 2018. Risk information recommendation for engineering workers. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence XXXV: proceedings of the 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in computer science, 11311. Cham: Springer [online], pages 311-325. Available from: https://doi.org/10.1007/978-3-030-04191-5_27

Within any sufficiently expertise-reliant and work-driven domain there is a requirement to understand the similarities between specific work tasks. Though mechanisms to develop similarity models for these areas do exist, in practice they have been cr... Read More about Risk information recommendation for engineering workers..

GramError: a quality metric for machine generated songs. (2018)
Conference Proceeding
DAVIES, C., WIRATUNGA, N. and MARTIN, K. 2018. GramError: a quality metric for machine generated songs. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence XXXV: proceedings of the 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in computer science, 11311. Cham: Springer [online], pages 184-190. Available from: https://doi.org/10.1007/978-3-030-04191-5_16

This paper explores whether a simple grammar-based metric can accurately predict human opinion of machine-generated song lyrics quality. The proposed metric considers the percentage of words written in natural English and the number of grammatical er... Read More about GramError: a quality metric for machine generated songs..

Context extraction for aspect-based sentiment analytics: combining syntactic, lexical and sentiment knowledge. (2018)
Conference Proceeding
BANDHAKAVI, A., WIRATUNGA, N., MASSIE, S. and LUHAR, R. 2018. Context extraction for aspect-based sentiment analytics: combining syntactic, lexical and sentiment knowledge. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence xxxv: proceedings of the 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in artificial intelligence, 11311. Cham: Springer [online], pages 357-371. Available from: https://doi.org/10.1007/978-3-030-04191-5_30

Aspect-level sentiment analysis of customer feedback data when done accurately can be leveraged to understand strong and weak performance points of businesses and services and also formulate critical action steps to improve their performance. In this... Read More about Context extraction for aspect-based sentiment analytics: combining syntactic, lexical and sentiment knowledge..

A holistic metric approach to solving the dynamic location-allocation problem. (2018)
Conference Proceeding
ANKRAH, R., LACROIX, B., MCCALL, J., HARDWICK, A. and CONWAY, A. 2018. A holistic metric approach to solving the dynamic location-allocation problem. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence xxxv: proceedings of the 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in artificial intelligence, 11311. Cham: Springer [online], pages 433-439. Available from: https://doi.org/10.1007/978-3-030-04191-5_35

In this paper, we introduce a dynamic variant of the Location-Allocation problem: Dynamic Location-Allocation Problem (DULAP). DULAP involves the location of facilities to service a set of customer demands over a defined horizon. To evaluate a soluti... Read More about A holistic metric approach to solving the dynamic location-allocation problem..

Overlap-based undersampling for improving imbalanced data classification. (2018)
Conference Proceeding
VUTTIPITTAYAMONGKOL, P., ELYAN, E., PETROVSKI, A. and JAYNE, C. 2018. Overlap-based undersampling for improving imbalanced data classification. In Yin, H., Camacho, D., Novais, P. and Tallón-Ballesteros, A. (eds.) Intelligent data engineering and automated learning: proceedings of the 19th International intelligent data engineering and automated learning conference (IDEAL 2018), 21-23 November 2018, Madrid, Spain. Lecture notes in computer science, 11341. Cham: Springer [online], pages 689-697. Available from: https://doi.org/10.1007/978-3-030-03493-1_72

Classification of imbalanced data remains an important field in machine learning. Several methods have been proposed to address the class imbalance problem including data resampling, adaptive learning and cost adjusting algorithms. Data resampling me... Read More about Overlap-based undersampling for improving imbalanced data classification..

Assessing system of systems security risk and requirements with OASoSIS. (2018)
Conference Proceeding
KI-ARIES, D., FAILY, S., DOGAN, H. and WILLIAMS, C. 2018. Assessing system of systems security risk and requirements with OASoSIS. In Beckers, K., Faily, S., Lee, S.-W. and Mead, N. (eds.) Proceedings of the 5th International workshop on evolving security and privacy requirements engineering (ESPRE 2018), 20 August 2018, Banff, Canada. Los Alamitos: IEEE Computer Society [online], pages 14-20. Available from: https://doi.org/10.1109/ESPRE.2018.00009

When independent systems come together as a System of Systems (SoS) to achieve a new purpose, dealing with requirements conflicts across systems becomes a challenge. Moreover, assessing and modelling security risk for independent systems and the SoS... Read More about Assessing system of systems security risk and requirements with OASoSIS..

Tool-supporting data protection impact assessments with CAIRIS. (2018)
Conference Proceeding
COLES, J., FAILY, S. and KI-ARIES, D. 2018. Tool-supporting data protection impact assessments with CAIRIS. In Beckers, K., Faily, S., Lee, S.-W. and Mead, N. (eds.) Proceedings of the 5th International workshop on evolving security and privacy requirements engineering (ESPRE 2018), 20 August 2018, Banff, Canada. Los Alamitos: IEEE Computer Society [online], pages 21-27. Available from: https://doi.org/10.1109/ESPRE.2018.00010

The General Data Protection Regulation (GDPR) encourages the use of Data Protection Impact Assessments (DPIAs) to integrate privacy into organisations' activities and practices from early design onwards. To date, however, there has been little prescr... Read More about Tool-supporting data protection impact assessments with CAIRIS..

Improving kNN for human activity recognition with privileged learning using translation models. (2018)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N., SANI, S., MASSIE, S. and COOPER, K. 2018. Improving kNN for human activity recognition with privileged learning using translation models. In Cox, M.T., Funk, P. and Begum, S. (eds.) Case-based reasoning research and development: proceedings of the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden. Lecture notes in computer science, 11156. Cham: Springer [online], pages 448-463. Available from: https://doi.org/10.1007/978-3-030-01081-2_30

Multiple sensor modalities provide more accurate Human Activity Recognition (HAR) compared to using a single modality, yet the latter is preferred by consumers as it is more convenient and less intrusive. This presents a challenge to researchers, as... Read More about Improving kNN for human activity recognition with privileged learning using translation models..